The availability of on-board electronics and in-vehicle information systems has accelerated the development of more intelligent vehicles. One possibility is to automatically monitor a driver's driving performance to prevent potential risks. Although protocols to measure a driver's workload have been developed by both government agencies and the automobile industry, they have been criticized as being too costly and difficult to obtain. In addition, existing uniform heuristics for driving risk preventions do not account for changes in individual driving environments. Hence technologies for understanding a driver's frustrations to prevent potential driving risks has been listed by many international automobile companies as one of the key research areas for realizing intelligent transportation systems.
In the past decade, researchers have attempted to recognize a driver's vigilance level by exploring physiological and bio-behavioral signals such as the brain wave, heart rate, blood volume pulse and respiration, etc. Acquiring physiological data, however, is intrusive because some electrodes or sensors must be attached to the driver's body.
To develop non-intrusive driving safety monitoring systems, two sets of features have been explored. The first is to extract visual features that are correlated to a driver's fatigue state, such as the head pose, face direction, head/eye movement tracks, etc, using cameras, infrared LEDs, or multiple visual sensors. Unfortunately, these visual features can not always be acquired accurately or reliably due to large human/environment variations and the immaturity of current computer vision techniques. The other set of non-intrusive features relates to a vehicle's dynamic parameters, such as lateral positions, steering wheel movements, accelerations/decelerations, etc. Since these parameters can be extracted using hidden sensors, and are strong indicators of a driver's driving state, they are promising features, and have big potentials for developing driving safety monitoring systems.
The task of detecting driving risks can be modeled as an anomaly detection problem. The most straightforward way of detecting driving anomalies is to use rule-based approaches where a set of heuristic rules are defined to reflect potential driving risks, and any violations of one or more rules are detected as anomalies. Defining a comprehensive set of rules to cover all kinds of driving conditions/risks, however, is an extremely difficult, or almost impossible task. Therefore, many researchers have applied statistical modeling methods, such as Fisher's Linear Discriminant Analysis, Support Vector Machines, and Bayesian Networks, to name a few, to attempt to learn a statistical model which is able to classify any driving state into either a safe or a dangerous state, and claim the detection of a driving anomaly whenever a driving state is classified as dangerous. In order to obtain a model with good classification accuracy, it is critical to create a large amount of training data that contains sufficient samples of safe and dangerous driving states.
Classification-based methods have several inherit problems. First, it is very difficult to collect sufficient samples of dangerous driving states, because typical drivers are in safe driving states for most of a driving session. Therefore, it is very likely that one will get a training set with extremely unbalanced positive and negative samples. Second, there are no commonly agreed-upon criteria to draw a clear boundary between safe and dangerous driving states. Third, the entire driving state space is indeed a continuous space where states corresponding to driving accidents scatter in many places, and any transitions between safe and dangerous states occur rather continuously than discretely. Simple, clear boundaries do not exist in the driving state space for objectively separating the safe states from the dangerous states.
Accordingly, an improved method is needed for detecting motor vehicle driving risks, conditions, or anomalies, that avoid the problems of prior art methods.